The present invention relates to a technique utilizing an array antenna; and, more particularly, to a method and apparatus for optimizing the beam pattern of an array antenna system and its applications to transmitting and receiving systems by finding a weight vector capable of maximizing the signal to interference plus noise ratio in as accurate and simple way as possible.
An antenna system that improves the performance of a wireless communication with a proper beam pattern is generally referred to as xe2x80x9csmart antenna system (SAS)xe2x80x9d. A theory related to the design of the SAS from a weight vector that maximizes the signal to interference plus noise ratio has been published in a prior art, [1]Ayman F. Naguib, xe2x80x9cAdaptive Antennas for CDMA Wireless Networksxe2x80x9d, Ph. D. Dissertation, Dept of Electrical Engineering, Stanford University, August 1996. In [1], the weight vector is obtained from an eigenvector corresponding to the largest eigenvalue of a generalized eigen-problem as follows:
Ryw=xcex7Ruwxe2x80x83xe2x80x83(1)
where Ry is the autocovariance matrix of the received (RX) signal vector y, which is obtained from the despreader output, Ru is the autocovariance matrix of the undesired signal vector u, and xcex and w are the eigenvalue and eigenvector of the eigen-problem shown in (1), respectively. In short, Ry=E[y yH] and Ru=E[u uH], where E[*] denotes the expectation of *.
Throughout this manuscript, vector and matrix quantities are written in lower case and upper case, respectively.
From [1], it can be observed that the optimal weight vector is the eigenvector corresponding to the largest eigenvalue of the eigen-problem shown in (1). However, since the autocovariance matrices Ry and Ru should be computed from the expectation of the power of y and u, it is never possible in actual signal environment to form the equation (1) at every snapshot for each of which the optimal weight vector should be computed. Even if the equation is formed somehow, since the computation of an eigenvector corresponding to the largest eigenvalue of a generalized eigen-problem requires a lot of computations, there arises many serious difficulties in applying the theory of [1] to the actual wireless communication world.
Recently, as the demand of the mobile communication and other wireless telecommunications increases rapidly, there arises an extremely keen need for developing the adaptive antenna array system that adopts an optimal weight vector in as accurate and simple way as possible. However, due to the limit of conventional techniques as mentioned above, it seems impossible to cope with the rapidly growing demand with the current techniques. Therefore, it is extremely and desperately required to develop a new technology for designing a smart antenna system enhancing the communication capacity and communication quality with a realizable complexity without loss of accuracy because of the simplicity.
In order to overcome the composite problems in the conventional techniques and eventually to cope with the drastically increasing demand of the wireless communications, this invention presents a simple and accurate way of computing the weight vector that maximizes the signal to interference plus noise ratio. By applying the smart antenna system designed by the technique provided in this invention into practical wireless communications, it is indeed possible to tremendously increase the communication capacity and enhance the communication quality. The objective of this invention is to suggest an optimal beam-forming method and communication apparatus that applies this method by providing the computation of an optimal weight vector for an antenna array system operating in a time-vary signal environment such as mobile communications.
In order to accomplish this objective, this invention provides a signal processing method and apparatus that generates a weight vector which maximizes the signal to interference plus noise ratio from the received signal vectors x and y, where x (pre-correlation signal vector) and y (post-correlation signal vector) are the received signal vectors obtained at the input and output of the correlators, i.e., despreading unit, which correlate the received signals with the PN (pseudo-random noise) code of the desired signal in a given CDMA system, respectively.